Recontextualized 4T1 RNA Seq - Survival Analysis based on GSVA Projections
Andrew Chen
August 04, 2023
library(Biobase)## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
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## table, tapply, union, unique, unsplit, which.max, which.min## Welcome to Bioconductor
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.library(survminer)## Loading required package: ggplot2## Loading required package: ggpubrlibrary(survival)##
## Attaching package: 'survival'## The following object is masked from 'package:survminer':
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theme_set(theme_gray(base_size = 25))
knitr::opts_chunk$set(dev = "png",
dpi = 300,
echo = FALSE,
fig.height = 6,
fig.width = 7,
cache = TRUE)
PATH <- file.path(Sys.getenv("MLAB"), "projects/brcameta/exosome/4t1_brca")
TCGAPATH <- file.path(Sys.getenv("CBM"), "TCGA-GDC/")
DPATH <- file.path(Sys.getenv("CBM"), "otherStudies/RNAseq/2023-03-22-YuhanExosomeBrCa")
do_save <- FALSELoading Data
Data Filtering
Censoring: If samples are alive, then they lived at least as long as the day to their last followup. For 5 year threshold, those who died after 5 years, should be right-censored, and labeled to have lived at least as long as ‘days to death’ + 1.
Survfit All subtypes
Adding GSVA data
IR_C_UP
IR_C
IS_C_UP
IS_C
IR_IS_UP
IR_IS
TGFB_C_UP
TGFB_C
Survfit Basal
Adding GSVA data
IR_C_UP
IR_C
IS_C_UP
IS_C
IR_IS_UP
IR_IS
TGFB_C_UP
TGFB_C
Cox Proportional Hazard
TCGA## Call:
## coxph(formula = Surv(as.numeric(time), vital_status_1) ~ ir_is +
## subtype_selected + age_at_index, data = pData(tcga_gsva))
##
## n= 1208, number of events= 199
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ir_is -0.151404 0.859501 0.251669 -0.602 0.54744
## subtype_selectedBRCA.Basal -0.740883 0.476693 0.283768 -2.611 0.00903 **
## subtype_selectedBRCA.Her2 -0.181146 0.834314 0.340328 -0.532 0.59454
## subtype_selectedBRCA.LumA -1.035346 0.355104 0.218367 -4.741 2.12e-06 ***
## subtype_selectedBRCA.LumB -0.610958 0.542830 0.265304 -2.303 0.02129 *
## age_at_index 0.034946 1.035564 0.005628 6.209 5.33e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## ir_is 0.8595 1.1635 0.5248 1.4076
## subtype_selectedBRCA.Basal 0.4767 2.0978 0.2733 0.8313
## subtype_selectedBRCA.Her2 0.8343 1.1986 0.4282 1.6256
## subtype_selectedBRCA.LumA 0.3551 2.8161 0.2315 0.5448
## subtype_selectedBRCA.LumB 0.5428 1.8422 0.3227 0.9130
## age_at_index 1.0356 0.9657 1.0242 1.0471
##
## Concordance= 0.697 (se = 0.022 )
## Likelihood ratio test= 67.24 on 6 df, p=2e-12
## Wald test = 70.12 on 6 df, p=4e-13
## Score (logrank) test = 72.23 on 6 df, p=1e-13## Call:
## coxph(formula = Surv(as.numeric(time), vital_status_1) ~ ir_is +
## Pam50_SUBTYPE + AGE_AT_DIAGNOSIS, data = pData(metabric_gsva))
##
## n= 1980, number of events= 1143
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ir_is -0.140774 0.868686 0.112651 -1.250 0.2114
## Pam50_SUBTYPEBasal 0.181927 1.199526 0.131293 1.386 0.1659
## Pam50_SUBTYPEHer2 0.279093 1.321930 0.132840 2.101 0.0356 *
## Pam50_SUBTYPELumA -0.216832 0.805065 0.114617 -1.892 0.0585 .
## Pam50_SUBTYPELumB 0.113128 1.119775 0.117410 0.964 0.3353
## Pam50_SUBTYPENC 0.106648 1.112543 0.459897 0.232 0.8166
## AGE_AT_DIAGNOSIS 0.036276 1.036942 0.002729 13.292 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## ir_is 0.8687 1.1512 0.6966 1.083
## Pam50_SUBTYPEBasal 1.1995 0.8337 0.9274 1.552
## Pam50_SUBTYPEHer2 1.3219 0.7565 1.0189 1.715
## Pam50_SUBTYPELumA 0.8051 1.2421 0.6431 1.008
## Pam50_SUBTYPELumB 1.1198 0.8930 0.8896 1.410
## Pam50_SUBTYPENC 1.1125 0.8988 0.4517 2.740
## AGE_AT_DIAGNOSIS 1.0369 0.9644 1.0314 1.043
##
## Concordance= 0.611 (se = 0.009 )
## Likelihood ratio test= 234.7 on 7 df, p=<2e-16
## Wald test = 224.2 on 7 df, p=<2e-16
## Score (logrank) test = 228.9 on 7 df, p=<2e-16